StackOverflow的人们,首先,感谢你们的耐心。我知道这是我关于这个主题的第三条线索,但由于我一事无成,我甚至不知道从哪里开始(我不知道我不知道什么),我想我还是在这里问一下。 我试图使用Biopython从PMC中提取引用,将其写回CSV文件,其中包括植物名称、它治疗的相关疾病/状况/其药用作用,以及引用给定植物疾病对的DOI URL。在花了很多时间试图理解该做什么,并与比我更有经验的人讨论代码之后,这就是最终在Visual Studio代码中键入的内容:
for plant, disease in plant_disease_list:
search_query = generate_search_query(plant, disease)
handle1 = Entrez.esearch(db="pmc", term=search_query, retmax="10")
record1 = Entrez.read(handle1)
pubmed_ids = record1.get("IdList")
if len(pubmed_ids)==0:
print("{}, {}, None".format(plant, disease))
else:
for pubmed_id in pubmed_ids:
handle2 = Entrez.esummary(db="pmc", id=pubmed_id)
records = Entrez.read(handle2)
for record in records:
doi = record.get("DOI")
if doi is None:
print(("{}, {}".format(plant, disease)))
else:
doi_main = doi.split()
string = "http://doi.org/"
to_add = (",").join((string + x) for x in doi_main)
print("{}, {},".format(plant, disease), to_add, sep="")
其中,生成搜索查询之前定义为:
def generate_search_query(plant, disease):
search_query = '"{}" AND "{}"'.format(plant, disease)
return search_query
这是我得到的输出:
Asystasia salicifalia, Puerperal illness, None
Asystasia salicifalia, Puerperium, None
Asystasia salicifalia, Puerperal disorder, None
Barleria strigosa, Tonic
Justicia procumbens, Lumbago, None
Justicia procumbens, Itching,http://doi.org/10.1673/031.012.0501
Strobilanthes auriculata, Malnutrition, None
Thunbergia laurifolia, Detoxificant, None
Thunbergia similis, Tonic, None
Lannea coromandelica, Dizziness,http://doi.org/10.3897/phytokeys.102.24380
Lannea coromandelica, Dizziness,http://doi.org/10.1186/s13002-016-0089-8
Lannea coromandelica, Dizziness,http://doi.org/10.1186/s13002-015-0033-3
Spondias pinnata, Flatulence,http://doi.org/10.1016/j.heliyon.2019.e02768
Spondias pinnata, Flatulence,http://doi.org/10.1186/s13002-019-0287-2
Spondias pinnata, Flatulence,http://doi.org/10.1186/s13002-018-0248-1
Spondias pinnata, Flatulence,http://doi.org/10.3897/phytokeys.102.24380
Spondias pinnata, Flatulence,http://doi.org/10.1155/2018/5382904
Spondias pinnata, Flatulence,http://doi.org/10.1186/s13002-016-0089-8
Spondias pinnata, Flatulence,http://doi.org/10.1186/s13002-015-0033-3
Spondias pinnata, Flatulence,http://doi.org/10.1186/1472-6882-13-243
Spondias pinnata, Flatulence,http://doi.org/10.1186/1472-6882-10-77
Holarrhena pubescens, Diarrhoea,http://doi.org/10.5455/javar.2019.f379
Holarrhena pubescens, Diarrhoea,http://doi.org/10.1155/2019/2321961
Holarrhena pubescens, Diarrhoea,http://doi.org/10.1186/s12906-018-2348-9
Traceback (most recent call last):
File "scraperscript_python.py", line 33, in <module>
handle2 = Entrez.esummary(db="pmc", id=pubmed_id)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\site-packages\Bio\Entrez\__init__.py", line 334, in esummary
return _open(cgi, variables)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\site-packages\Bio\Entrez\__init__.py", line 569, in _open
handle = _urlopen(cgi)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\urllib\request.py", line 222, in urlopen
return opener.open(url, data, timeout)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\urllib\request.py", line 525, in open
response = self._open(req, data)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\urllib\request.py", line 543, in _open
'_open', req)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\urllib\request.py", line 503, in _call_chain
result = func(*args)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\urllib\request.py", line 1362, in https_open
context=self._context, check_hostname=self._check_hostname)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\urllib\request.py", line 1319, in do_open
encode_chunked=req.has_header('Transfer-encoding'))
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\http\client.py", line 1252, in request
self._send_request(method, url, body, headers, encode_chunked)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\http\client.py", line 1298, in _send_request
self.endheaders(body, encode_chunked=encode_chunked)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\http\client.py", line 1247, in endheaders
self._send_output(message_body, encode_chunked=encode_chunked)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\http\client.py", line 1026, in _send_output
self.send(msg)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\http\client.py", line 966, in send
self.connect()
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\http\client.py", line 1422, in connect
server_hostname=server_hostname)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\ssl.py", line 423, in wrap_socket
session=session
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\ssl.py", line 870, in _create
self.do_handshake()
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python37\lib\ssl.py", line 1139, in do_handshake
self._sslobj.do_handshake()
KeyboardInterrupt
其余的输出被我中断了,因为我不希望它在整个数据上运行,因为它正在以错误的形式打印它。正如您可以看到的羽状海绵和肠胃胀气的例子一样,您可以看到它在不同的行中打印不同的DOI URL。问题是我不希望它像那样打印,因为要把它放回原始数据中是非常困难的。例如,这个CSV文件只有65个条目,但有超过8000个条目的数据集,这使它成为一项非常困难的工作。例如,当我考虑上述植物病害对时,我希望得到的输出是这样的:
Spondias pinnata, Flatulence, http://doi.org/10.1016/j.heliyon.2019.e02768, http://doi.org/10.1186/s13002-019-0287-2, http://doi.org/10.1186/s13002-018-0248-1, http://doi.org/10.3897/phytokeys.102.24380, http://doi.org/10.1155/2018/5382904, http://doi.org/10.1186/s13002-016-0089-8, http://doi.org/10.1186/s13002-015-0033-3, http://doi.org/10.1186/1472-6882-13-243, http://doi.org/10.1186/1472-6882-10-77
我的家人建议我使用嵌套字典,但我不知道这会有什么帮助,我也不知道应该把它放在代码中的什么地方,以及对已经嵌套得很厉害的循环要做什么更改。在此方面的任何帮助都将不胜感激。多谢各位
以下代码:
将以下输出写入文件
plant_diseases.csv
:注意,我使用了
csv
模块来创建valid CSV文件。这包括在逗号分隔的DOI列表周围添加双QOUTE,以将它们与用于描述植物和疾病的逗号分隔开。此外,如果没有DOI,则无需添加None占位符。由于第一行包含一个标题,csv
模块知道它应该在那里每行查找三个字段另外,不要使用
string
作为变量名,因为它是a Python module in the standard library的名称相关问题 更多 >
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